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Quantifying Counts, Costs, and Trends Accurately via Machine Learning
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VW(1) User Commands VW(1) NAME
vw - Vowpal Wabbit -- fast online learning tool DESCRIPTION
VW options: -h [ --help ] Look here: http://hunch.net/~vw/ and click on Tutorial. --active_learning active learning mode --active_simulation active learning simulation mode --active_mellowness arg (=8) active learning mellowness parameter c_0. Default 8 --adaptive use adaptive, individual learning rates. --exact_adaptive_norm use a more expensive exact norm for adaptive learning rates. -a [ --audit ] print weights of features -b [ --bit_precision ] arg number of bits in the feature table --bfgs use bfgs optimization -c [ --cache ] Use a cache. The default is <data>.cache --cache_file arg The location(s) of cache_file. --compressed use gzip format whenever possible. If a cache file is being created, this option creates a compressed cache file. A mixture of raw-text & compressed inputs are supported with autodetection. --conjugate_gradient use conjugate gradient based optimization --nonormalize Do not normalize online updates --l1 arg (=0) l_1 lambda --l2 arg (=0) l_2 lambda -d [ --data ] arg Example Set --daemon persistent daemon mode on port 26542 --num_children arg (=10) number of children for persistent daemon mode --pid_file arg Write pid file in persistent daemon mode --decay_learning_rate arg (=1) Set Decay factor for learning_rate between passes --input_feature_regularizer arg Per feature regularization input file -f [ --final_regressor ] arg Final regressor --readable_model arg Output human-readable final regressor --hash arg how to hash the features. Available options: strings, all --hessian_on use second derivative in line search --version Version information --ignore arg ignore namespaces beginning with character <arg> --initial_weight arg (=0) Set all weights to an initial value of 1. -i [ --initial_regressor ] arg Initial regressor(s) --initial_pass_length arg (=18446744073709551615) initial number of examples per pass --initial_t arg (=1) initial t value --lda arg Run lda with <int> topics --lda_alpha arg (=0.100000001) Prior on sparsity of per-document topic weights --lda_rho arg (=0.100000001) Prior on sparsity of topic distributions --lda_D arg (=10000) Number of documents --minibatch arg (=1) Minibatch size, for LDA --span_server arg Location of server for setting up spanning tree --min_prediction arg Smallest prediction to output --max_prediction arg Largest prediction to output --mem arg (=15) memory in bfgs --noconstant Don't add a constant feature --noop do no learning --output_feature_regularizer_binary arg Per feature regularization output file --output_feature_regularizer_text arg Per feature regularization output file, in text --port arg port to listen on --power_t arg (=0.5) t power value -l [ --learning_rate ] arg (=10) Set Learning Rate --passes arg (=1) Number of Training Passes --termination arg (=0.00100000005) Termination threshold -p [ --predictions ] arg File to output predictions to -q [ --quadratic ] arg Create and use quadratic features --quiet Don't output diagnostics --rank arg (=0) rank for matrix factorization. --random_weights arg make initial weights random -r [ --raw_predictions ] arg File to output unnormalized predictions to --save_per_pass Save the model after every pass over data --sendto arg send examples to <host> -t [ --testonly ] Ignore label information and just test --loss_function arg (=squared) Specify the loss function to be used, uses squared by default. Currently available ones are squared, classic, hinge, logistic and quantile. --quantile_tau arg (=0.5) Parameter au associated with Quantile loss. Defaults to 0.5 --unique_id arg (=0) unique id used for cluster parallel jobs --total arg (=1) total number of nodes used in cluster parallel job --node arg (=0) node number in cluster parallel job --sort_features turn this on to disregard order in which features have been defined. This will lead to smaller cache sizes --ngram arg Generate N grams --skips arg Generate skips in N grams. This in conjunction with the ngram tag can be used to generate generalized n-skip-k-gram. vw 6.1 June 2012 VW(1)
